| Paper: | TP-P4.12 |
| Session: | Image and Video Segmentation V |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
THE HOUGH TRANSFORM'S IMPLICIT BAYESIAN FOUNDATION |
| Authors: |
Neil Toronto; Brigham Young University | | |
| | Bryan Morse; Brigham Young University | | |
| | Dan Ventura; Brigham Young University | | |
| | Kevin Seppi; Brigham Young University | | |
| Abstract: |
This paper shows that the basic Hough transform is implicitly a Bayesian process---that it computes an unnormalized posterior distribution over the parameters of a single shape given feature points. The proof motivates a purely Bayesian approach to the problem of finding parameterized shapes in digital images. A proof-of-concept implementation that finds multiple shapes of four parameters is presented. Extensions to the basic model that are made more obvious by the presented reformulation are discussed. |
